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Journal : Journal of Computer Networks, Architecture and High Performance Computing

Naive Bayes Algorithm for Sentiment Analysis on Spider-Man Movie: No Way Home: Data Mining Makarim, Ziddan; Nawangsih, Ismasari; Sanudin, Sanudin
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i4.4845

Abstract

The rapid development of streaming platforms has significantly changed the landscape of movie consumption. The ease of access and social interaction in online communities has led to the creation of a new pop culture around movies. One interesting phenomenon is the movie Spider-Man: No Way Home, which sparked heated and viral conversations on various social media platforms. This research aims to analyze audience sentiment towards the movie Spider-Man: No Way Home using Naïve Bayes algorithm. Review data collected from online platforms was processed to identify positive and negative sentiments. The choice of Naïve Bayes algorithm is based on its efficiency and ability to classify text. The results showed that the model built was able to classify sentiment with an accuracy of 72.34%. The model is more effective in identifying positive reviews than negative, indicating a positive response from the majority of viewers. However, the model still needs to improve its performance in classifying negative sentiments. This research makes an important contribution in understanding audience preferences and evaluating the success of a movie, especially in the context of the digital era. The results can be utilized by the film industry to improve production quality, marketing strategies, and content development that is more relevant to audience preferences. In addition, this research also opens up opportunities for further development, such as the use of more complex algorithms or combining with other sentiment analysis techniques, as well as application to various types of social media content.
Naive Bayes Algorithm for Sentiment Analysis on Spider-Man Movie: No Way Home: Data Mining Makarim, Ziddan; Nawangsih, Ismasari; Sanudin, Sanudin
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i4.4845

Abstract

The rapid development of streaming platforms has significantly changed the landscape of movie consumption. The ease of access and social interaction in online communities has led to the creation of a new pop culture around movies. One interesting phenomenon is the movie Spider-Man: No Way Home, which sparked heated and viral conversations on various social media platforms. This research aims to analyze audience sentiment towards the movie Spider-Man: No Way Home using Naïve Bayes algorithm. Review data collected from online platforms was processed to identify positive and negative sentiments. The choice of Naïve Bayes algorithm is based on its efficiency and ability to classify text. The results showed that the model built was able to classify sentiment with an accuracy of 72.34%. The model is more effective in identifying positive reviews than negative, indicating a positive response from the majority of viewers. However, the model still needs to improve its performance in classifying negative sentiments. This research makes an important contribution in understanding audience preferences and evaluating the success of a movie, especially in the context of the digital era. The results can be utilized by the film industry to improve production quality, marketing strategies, and content development that is more relevant to audience preferences. In addition, this research also opens up opportunities for further development, such as the use of more complex algorithms or combining with other sentiment analysis techniques, as well as application to various types of social media content.
Co-Authors Adi Rusdi Widya Adi Tio Ilhasa Agus Setiawan Agus Suwarno, Agus Ahmad Gunawan Ahmad Tholud Ahmad Tholud2 Ahmad, Asyari Al Bina, Fiqhy Faradisa Amali Amali Amali, Amali Andri Firmansyah Anggi Muhammad Rifa’i Annisa Maulana Majid Annisa Maulana Majid Antika Zahrotul Kamalia Antika Zahrotul Kamalia Ardi Gunawan Arfan, Ibnu Soffi Asep Arwan Sulaeman Asti Setyaningsih Asty Setyaningsih Avifah Dian Permatasari Badruzzaman, Aceng Budi Rahardjo, Sugeng Budiarto , Eko Budiarto, Eko Cecep Wiranto chandra H, Desy Djoko Nugroho Donny Maulana Edora Edora Edy Widodo Edy Widodo Elgi Ginanjar Fazri Setyawan Ferawati, Eva Gatot Tri Pranoto Hari Puji Saputro Indradewa, Rhian Ismamudi Ismamudi Ismamudi, Ismamudi Junisa Sahar Karina Imelda Khaliq, Achsyanul Kurniadi, Nanang Tedi Majid, Annisa Maulana Majid, Annisa Maulana Makarim, Ziddan Maulana Majid, Annisa Maulana, Donny Miftahul Jannah Miftakul Huda Muhamad Adhi Mukti Naya, Candra Pupung Purnamasari Pupung Purnamasari Putri Maharani, Nanda Rachmat Hidayat Rahardjo, Sugeng Budi Reza Puspita Riady, Sasmitoh Rahmad Rianti Kinasih Rismawati Sanudin Sanudin Sellina, Sesri Septian Arie Prayoga Sifa Fauziah Siti Rahayu SITI SETIAWATI Soer, U. Darmanto Soer, U. Darmanto Sri Rejeki Sugeng Budi Rahardjo Sugeng Budi Raharjo Suherman Suherman Sulaeman, Asep Arwan Supriyati . Suriyanti Susanto, Dede Agus Suwaryo, Niko Tedi, Nanang Tri Ngudi Wiyatno W, Wiyanto Wahyu Hadikristanto Widodo , Edy Widya, Adi Rusdi Wiyanto Wiyanto Wiyanto Wiyatno, Tringudi Zed, Etty Zuliawati Zuliawati zed, Etty